
Call:
mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept) (Intercept) 
          0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


Call:
mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m2" 

Fixed effects:
(Intercept) (Intercept) 
          0           0 


Random effects covariance matrix:
$id
                                 m2
                        (Intercept)
         m2 (Intercept)           0


Call:
mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept)          C1 (Intercept)          C1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


Call:
mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m2" 

Fixed effects:
(Intercept)          C1 (Intercept)          C1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m2
                        (Intercept)
         m2 (Intercept)           0


Call:
mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept) (Intercept)          c1          c1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


Call:
mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m2" 

Fixed effects:
(Intercept) (Intercept)          c1          c1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m2
                        (Intercept)
         m2 (Intercept)           0


Call:
mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept)          C2 (Intercept)          C2 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


Call:
mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m2" 

Fixed effects:
(Intercept)          C2 (Intercept)          C2 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m2
                        (Intercept)
         m2 (Intercept)           0


Call:
mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept) (Intercept)          c2          c2 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


Call:
mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m2" 

Fixed effects:
(Intercept) (Intercept)          c2          c2 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m2
                        (Intercept)
         m2 (Intercept)           0


Call:
lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)         m1B         m1C 
          0           0           0 


Random effects covariance matrix:
$id
                                 c1
                        (Intercept)
         c1 (Intercept)           0



Residual standard deviation:
sigma_c1 
       0 

Call:
lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)         m2B         m2C 
          0           0           0 


Random effects covariance matrix:
$id
                                 c1
                        (Intercept)
         c1 (Intercept)           0



Residual standard deviation:
sigma_c1 
       0 

Call:
mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) + 
    (1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
     (Intercept)              M22              M23              M24 
               0                0                0                0 
    abs(C1 - C2)          log(C1)      (Intercept)              M22 
               0                0                0                0 
             M23              M24     abs(C1 - C2)          log(C1) 
               0                0                0                0 
             m2B              m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) 
               0                0                0                0 
             m2B              m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) 
               0                0                0                0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


Call:
mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1), 
    1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
                                               (Intercept) 
                                                         0 
                                              abs(C1 - C2) 
                                                         0 
                                                   log(C1) 
                                                         0 
                                               (Intercept) 
                                                         0 
                                              abs(C1 - C2) 
                                                         0 
                                                   log(C1) 
                                                         0 
             ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 
                                                         0 
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 
                                                         0 
             ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 
                                                         0 
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 
                                                         0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


Call:
mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time | 
    id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept)          C1         B21 (Intercept)          C1         B21 
          0           0           0           0           0           0 
       time          c1        time          c1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1          m1          m1          m1
                        (Intercept)          c1        time     c1:time
         m1 (Intercept)           0           0           0           0
         m1          c1           0           0           0           0
         m1        time           0           0           0           0
         m1     c1:time           0           0           0           0


Call:
mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, 
    random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept)          C1 (Intercept)          C1        time   I(time^2) 
          0           0           0           0           0           0 
        b21          c1     C1:time      b21:c1        time   I(time^2) 
          0           0           0           0           0           0 
        b21          c1     C1:time      b21:c1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1          m1
                        (Intercept)        time
         m1 (Intercept)           0           0
         m1        time           0           0


Call:
mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, 
    random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept)          C1 (Intercept)          C1   log(time)   I(time^2) 
          0           0           0           0           0           0 
         p1   log(time)   I(time^2)          p1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0

$m0a

Call:
mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept) (Intercept) 
          0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


$m0b

Call:
mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m2" 

Fixed effects:
(Intercept) (Intercept) 
          0           0 


Random effects covariance matrix:
$id
                                 m2
                        (Intercept)
         m2 (Intercept)           0


$m1a

Call:
mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept)          C1 (Intercept)          C1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


$m1b

Call:
mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m2" 

Fixed effects:
(Intercept)          C1 (Intercept)          C1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m2
                        (Intercept)
         m2 (Intercept)           0


$m1c

Call:
mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept) (Intercept)          c1          c1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


$m1d

Call:
mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m2" 

Fixed effects:
(Intercept) (Intercept)          c1          c1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m2
                        (Intercept)
         m2 (Intercept)           0


$m2a

Call:
mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept)          C2 (Intercept)          C2 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


$m2b

Call:
mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m2" 

Fixed effects:
(Intercept)          C2 (Intercept)          C2 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m2
                        (Intercept)
         m2 (Intercept)           0


$m2c

Call:
mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept) (Intercept)          c2          c2 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


$m2d

Call:
mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m2" 

Fixed effects:
(Intercept) (Intercept)          c2          c2 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m2
                        (Intercept)
         m2 (Intercept)           0


$m3a

Call:
lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)         m1B         m1C 
          0           0           0 


Random effects covariance matrix:
$id
                                 c1
                        (Intercept)
         c1 (Intercept)           0



Residual standard deviation:
sigma_c1 
       0 

$m3b

Call:
lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)         m2B         m2C 
          0           0           0 


Random effects covariance matrix:
$id
                                 c1
                        (Intercept)
         c1 (Intercept)           0



Residual standard deviation:
sigma_c1 
       0 

$m4a

Call:
mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) + 
    (1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
     (Intercept)              M22              M23              M24 
               0                0                0                0 
    abs(C1 - C2)          log(C1)      (Intercept)              M22 
               0                0                0                0 
             M23              M24     abs(C1 - C2)          log(C1) 
               0                0                0                0 
             m2B              m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) 
               0                0                0                0 
             m2B              m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2) 
               0                0                0                0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


$m4b

Call:
mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1), 
    1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
                                               (Intercept) 
                                                         0 
                                              abs(C1 - C2) 
                                                         0 
                                                   log(C1) 
                                                         0 
                                               (Intercept) 
                                                         0 
                                              abs(C1 - C2) 
                                                         0 
                                                   log(C1) 
                                                         0 
             ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 
                                                         0 
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 
                                                         0 
             ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 
                                                         0 
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 
                                                         0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


$m4c

Call:
mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time | 
    id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept)          C1         B21 (Intercept)          C1         B21 
          0           0           0           0           0           0 
       time          c1        time          c1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1          m1          m1          m1
                        (Intercept)          c1        time     c1:time
         m1 (Intercept)           0           0           0           0
         m1          c1           0           0           0           0
         m1        time           0           0           0           0
         m1     c1:time           0           0           0           0


$m4d

Call:
mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, 
    random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept)          C1 (Intercept)          C1        time   I(time^2) 
          0           0           0           0           0           0 
        b21          c1     C1:time      b21:c1        time   I(time^2) 
          0           0           0           0           0           0 
        b21          c1     C1:time      b21:c1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1          m1
                        (Intercept)        time
         m1 (Intercept)           0           0
         m1        time           0           0


$m4e

Call:
mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, 
    random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian multinomial logit mixed model for "m1" 

Fixed effects:
(Intercept)          C1 (Intercept)          C1   log(time)   I(time^2) 
          0           0           0           0           0           0 
         p1   log(time)   I(time^2)          p1 
          0           0           0           0 


Random effects covariance matrix:
$id
                                 m1
                        (Intercept)
         m1 (Intercept)           0


